An introduction to genetic algorithms

@inproceedings{Mitchell1996AnIT,
  title={An introduction to genetic algorithms},
  author={Melanie Mitchell},
  year={1996}
}
From the Publisher: "This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If… Expand
Chapter 3 Introduction to using genetic algorithms
TLDR
This chapter discusses how algorithm might be used to solve problems in earth sciences, set up a Genetic Algorithm (GA), and be aware of the design issues involved in its use. Expand
On the practical usage of genetic algorithms in ecology and evolution
TLDR
While genetic algorithms offer great power and flexibility by drawing inspiration from evolutionary processes, they are (usually) not a faithful model of genetics or evolution. Expand
Genetic algorithms
TLDR
A genetic algorithm is a computational model of biological evolution that describes how binary strings are stored in a computer’s memory and over time are modified in much the same way that populations of individuals evolve under natural selection. Expand
Genetic algorithms
TLDR
Genetic algorithms (GAs) are computational search, learning, optimization, and modeling methods, loosely inspired by biological evolution, which can inspire computational search methods for finding solutions to hard problems in large search spaces or for designing complex systems automatically. Expand
A more realistic genetic algorithm
Genetic Algorithms (GAs) are loosely based on the concept of the natural cycle of reproduction with selective pressures favouring the individuals which are best suited to their environment (i.e.Expand
Controlling the Population Size in Genetic Programming
TLDR
A method that controls the population size in a GA is adapted and implemented in GP, and a series of classic experiments have been performed before and after the modifications, showing that this method can improve the algorithms' robustness and reliability. Expand
Using genetic algorithms to solve optimization problems in construction
TLDR
This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature, and suggests general guidelines to develop solutions using this optimization technique. Expand
A Review of Genetic Algorithms in Power Engineering
Genetic algorithm is a search and optimisation method simulating natural selection and genetics. It is the most popular and widely used of all evolutionary algorithms. Genetic algorithms, in one formExpand
A Methodology for the Statistical Characterization of Genetic Algorithms
TLDR
A methodology which aims at achieving a solid relative evaluation of alternative GAs by resorts to statistical arguments and may categorize any iterative optimization algorithm by statistically finding the basic parameters of the probability distribution of the GA’s optimum values without resorting to a priori functions is presented. Expand
Learning by Genetic Algorithms in Economics
Although it was not the context the ‘founder’ of the Genetic Algotithm (GA), J.H. HOLLAND (1975/1992)1 had in mind, most of the research effort has been spent by specifying the GA as a functionExpand
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References

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TLDR
This dissertation describes an empirical investigation into whether it can be convincingly argued that these probabilities should vary over the course of a genetic algorithm run so as to account for changes in the ability of the operators to produce children of increased strength. Expand
Handbook Of Genetic Algorithms
TLDR
This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem. Expand
Messy Genetic Algorithms: Motivation, Analysis, and First Results
TLDR
The mGA presented herein repeatedly achieves globally optimal results without prior knowledge of good string arrangements, and it does so at the very first generation in which strings are long enough to cover the problem. Expand
Distributed genetic algorithms for function optimization
TLDR
This dissertation proposes a parallelized version of a genetic algorithm called the distributed genetic algorithm, which can achieve near-linear speedup over the traditional version of the algorithm, and discusses the issue of balancing exploration against exploitation in the distributed Genetic algorithm, by allowing different subpopulations to run with different parameters, so that someSubpopulations can emphasize exploration while others emphasize exploitation. Expand
The evolution of evolvability in genetic programming
TLDR
Several new selection techniques and genetic operators are proposed in order to give better control over the evolution of evolvability and improved evolutionary performance. Expand
Genetic Algorithms for Real Parameter Optimization
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  • 1990
TLDR
It is shown that k-point crossover can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters, which suggests a genetic algorithm that uses real parameter vectors as chromosomes, real parameters as genes, and real numbers as alleles. Expand
Genetic Programming II: Automatic Discovery of Reusable Programs.
TLDR
This book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. Expand
Recombination Distributions for Genetic Algorithms
TLDR
This paper reviews some well known results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection and uses this characterization to quantify certain inductive biases associated with crossover operators. Expand
Genetic programming - on the programming of computers by means of natural selection
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TLDR
This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming. Expand
Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale
TLDR
Although additional basic work is both needed and recommended, the compelling convergence and efficiency demonstrated by mGAs recommends them for immediate application in some of the many tough, blind combinatorial optimization problems of science and engineering that have gone unsolved for want of more tractable solution techniques. Expand
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